Data Compression Using a Sort-Based Context Similarity Measure

Abstract

Every symbol in the data can be predicted by taking the immediately preceding symbols, or context, into account. This paper proposes a new adaptive data-compression method based on a context similarity measure. We measure the similarity of contexts using a context sorting mechanism. The aim of context sorting is to store a set of contexts in a specific order so that contexts more similar to the current one are more accessible. The proposed method predicts the next symbol by ranking the previous context-symbol pairs in order of context similarity. The codeword for the next symbol represents the rank of the symbol in this ordered sequence. The compression performance is evaluated both analytically and empirically. Although the proposed method uses no probability distribution to make a prediction, it gains good compression. It also reveals a strong relation between symbol-ranking compression and the Ziv-Lempel textual substitution method.